Chapter 16: Performance of DLNN — Comparative Case Studies

This chapter discusses the performance of the Deep-Learning neural networks and specifically the networks of Chaps. 14 and 15. It will do so using 20 case studies over a wide range of applications, which were performed by graduate students in the author’s class. The chapter will also compare the computational speeds of these networks, as is most important for any real-time online application (as a part of a controller, or in many applications in medical devices, medical diagnosis, and in many traffic of financial applications. The chapter also aims to provide better understanding of how these networks are applied in a wide range of applications in the above areas and beyond. Conclusion drawn from these 20 Case Studies are certainly not representative of the huge literature on deep learning neural networks. Especially, they may not be applicable to their applications to huge data bases (some of which is mentioned in Chap. 14, with respect to CNN applications). However, hardly any comparison is available in the literature, at least, not for comparing CNN and LAMSTAR, and certainly not for the same input data. And no comparison is of value if the data are not the same for the networks being compared. In contrast, the present case studies were specially designed to do just this, namely to apply several DLNN networks with the exact same data to a broad range of applications and with the same computing device (as is essential when evaluating computing speed). Further comparisons on very large databases are certainly called for.